Toward fully automated genotyping: genotyping microsatellite markers by deconvolution.

Dense genetic linkage maps have been constructed for the human and mouse genomes, with average densities of 2.9 cM and 0.35 cM, respectively. These genetic maps are crucial for mapping both Mendelian and complex traits and are useful in clinical genetic diagnosis. Current maps are largely comprised of abundant, easily assayed, and highly polymorphic PCR-based microsatellite markers, primarily dinucleotide (CA)n repeats. One key limitation of these length polymorphisms is the PCR stutter (or slippage) artifact that introduces additional stutter bands. With two (or more) closely spaced alleles, the stutter bands overlap, and it is difficult to accurately determine the correct alleles; this stutter phenomenon has all but precluded full automation, since a human must visually inspect the allele data. We describe here novel deconvolution methods for accurate genotyping that mathematically remove PCR stutter artifact from microsatellite markers. These methods overcome the manual interpretation bottleneck and thereby enable full automation of genetic map construction and use. New functionalities, including the pooling of DNAs and the pooling of markers, are described that may greatly reduce the associated experimentation requirements.     

The SNPlex genotyping system: a flexible and scalable platform for SNP genotyping.

We developed the SNPlex Genotyping System to address the need for accurate genotyping data, high sample throughput, study design flexibility, and cost efficiency. The system uses oligonucleotide ligation/polymerase chain reaction and capillary electrophoresis to analyze bi-allelic single nucleotide polymorphism genotypes. It is well suited for single nucleotide polymorphism genotyping efforts in which throughput and cost efficiency are essential. The SNPlex Genotyping System offers a high degree of flexibility and scalability, allowing the selection of custom-defined sets of SNPs for medium- to high-throughput genotyping projects. It is therefore suitable for a broad range of study designs. In this article we describe the principle and applications of the SNPlex Genotyping System, as well as a set of single nucleotide polymorphism selection tools and validated assay resources that accelerate the assay design process. We developed the control pool, an oligonucleotide ligation probe set for training and quality-control purposes, which interrogates 48 SNPs simultaneously. We present performance data from this control pool obtained by testing genomic DNA samples from 44 individuals. in addition, we present data from a study that analyzed 521 SNPs in 92 individuals. Combined, both studies show the SNPlex Genotyping system to have a 99.32% overall call rate, 99.95% precision, and 99.84% concordance with genotypes analyzed by TaqMan probe-based assays. The SNPlex Genotyping System is an efficient and reliable tool for a broad range of genotyping applications, supported by applications for study design, data analysis, and data management.     

Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations

Genotyping by sequencing (GBS) is the latest application of next-generation sequencing protocols for the purposes of discovering and genotyping SNPs in a variety of crop species and populations. Unlike other high-density genotyping technologies which have mainly been applied to general interest “reference” genomes, the low cost of GBS makes it an attractive means of saturating mapping and breeding populations with a high density of SNP markers. One barrier to the widespread use of GBS has been the difficulty of the bioinformatics analysis as the approach is accompanied by a high number of erroneous SNP calls which are not easily diagnosed or corrected. In this study, we use a 384-plex GBS protocol to add 30,984 markers to an indica (IR64) × japonica (Azucena) mapping population consisting of 176 recombinant inbred lines of rice (Oryza sativa) and we release our imputation and error correction pipeline to address initial GBS data sparsity and error, and streamline the process of adding SNPs to RIL populations. Using the final imputed and corrected dataset of 30,984 markers, we were able to map recombination hot and cold spots and regions of segregation distortion across the genome with a high degree of accuracy, thus identifying regions of the genome containing putative sterility loci. We mapped QTL for leaf width and aluminum tolerance, and were able to identify additional QTL for both phenotypes when using the full set of 30,984 SNPs that were not identified using a subset of only 1,464 SNPs, including a previously unreported QTL for aluminum tolerance located directly within a recombination hotspot on chromosome 1. These results suggest that adding a high density of SNP markers to a mapping or breeding population through GBS has a great value for numerous applications in rice breeding and genetics research.     

The power and promise of population genomics: from genotyping to genome typing

Population genomics has the potential to improve studies of evolutionary genetics, molecular ecology and conservation biology, by facilitating the identification of adaptive molecular variation and by improving the estimation of important parameters such as population size, migration rates and phylogenetic relationships. There has been much excitement in the recent literature about the identification of adaptive molecular variation using the population-genomic approach. However, the most useful contribution of the genomics model to population genetics will be improving inferences about population demography and evolutionary history.     

BeadArray technology: enabling an accurate,cost-effective approach to high-throughput genotyping

The Human Genome Project has opened the door to personalized medicine, provided that human genetic diversity can be analyzed in a high-throughput and cost-effective way Illumina has developed a genotyping system that combines very high throughput and accuracy with low cost per SNP analysis. The system uses our BeadArray platform, a high level of multiplexing, and modular, scalable automation to meet the requirements for cost-effective, genome-wide linkage disequilibrium studies. As implemented in a high-throughput genotyping service facility at Illumina, the system has a current capacity of one million SNP assays per day and is easily expandable. Each SNP call is associated with a quality score that correlates with accuracy     

PCR candidate region mismatch scanning: adaptation to quantitative, high-throughput genotyping

Linkage and association analyses were performed to identify loci affecting disease susceptibility by scoring previously characterized sequence variations such as microsatellites and single nucleotide polymorphisms. Lack of markers in regions of interest, as well as difficulty in adapting various methods to high-throughput settings, often limits the effectiveness of the analyses. We have adapted the Escherichia coli mismatch detection system, employing the factors MutS, MutL and MutH, for use in PCR-based, automated, high-throughput genotyping and mutation detection of genomic DNA. Optimal sensitivity and signal-to-noise ratios were obtained in a straightforward fashion because the detection reaction proved to be principally dependent upon monovalent cation concentration and MutL concentration. Quantitative relationships of the optimal values of these parameters with length of the DNA test fragment were demonstrated, in support of the translocation model for the mechanism of action of these enzymes, rather than the molecular switch model. Thus, rapid, sequence-independent optimization was possible for each new genomic target region. Other factors potentially limiting the flexibility of mismatch scanning, such as positioning of dam recognition sites within the target fragment, have also been investigated. We developed several strategies, which can be easily adapted to automation, for limiting the analysis to intersample heteroduplexes. Thus, the principal barriers to the use of this methodology, which we have designated PCR candidate region mismatch scanning, in cost-effective, high-throughput settings have been removed.     

Susceptibility of biallelic haplotype and genotype frequencies to genotyping error

With the availability of fast genotyping methods and genomic databases, the search for statistical association of single nucleotide polymorphisms with a complex trait has become an important methodology in medical genetics. However, even fairly rare errors occurring during the genotyping process can lead to spurious association results and decrease in statistical power. We develop a systematic approach to study how genotyping errors change the genotype distribution in a sample. The general M-marker case is reduced to that of a single-marker locus by recognizing the underlying tensor-product structure of the error matrix. Both method and general conclusions apply to the general error model; we give detailed results for allele-based errors of size depending both on the marker locus and the allele present. Multiple errors are treated in terms of the associated diffusion process on the space of genotype distributions. We find that certain genotype and haplotype distributions remain unchanged under genotyping errors, and that genotyping errors generally render the distribution more similar to the stable one. In case-control association studies, this will lead to loss of statistical power for nondifferential genotyping errors and increase in type I error for differential genotyping errors. Moreover, we show that allele-based genotyping errors do not disturb Hardy-Weinberg equilibrium in the genotype distribution. In this setting we also identify maximally affected distributions. As they correspond to situations with rare alleles and marker loci in high linkage disequilibrium, careful checking for genotyping errors is advisable when significant association based on such alleles/haplotypes is observed in association studies.     

Nephele: genotyping via complete composition vectors and MapReduce

Background

Current sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences.

Results

Nephele is a suite of tools that uses the complete composition vector algorithm to represent each sequence in the dataset as a vector derived from its constituent k-mers by passing the need for multiple sequence alignment, and affinity propagation clustering to group the sequences into genotypes based on a distance measure over the vectors. Our methods produce results that correlate well with expert-defined clades or genotypes, at a fraction of the computational cost of traditional phylogenetic methods run on traditional hardware. Nephele can use the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. We were able to generate a neighbour-joined tree of over 10,000 16S samples in less than 2 hours.

Conclusions

We conclude that using Nephele can substantially decrease the processing time required for generating genotype trees of tens to hundreds of organisms at genome scale sequence coverage.     

Anchoring of unmodified oligonucleotides on a chitosan-based array: applications to genotyping and gene expression

Oligonucleotide arrays are one of the most used technologies in genotyping and gene expression experiments. Here, a previously developed chitosan-based platform is characterized in its binding of unmodified oligonucleotides, increasing the cost effectiveness of the microarray production process. The unmodified oligonucleotides on the activated chitosan surface showed the same or better performances than amino-modified probes. Moreover, we show applications in genotyping (a ligation detection reaction experiment on cyanobacteria) and in gene expression (on peach RNA samples). The platform was demonstrated to be reliable, robust, and reproducible for immobilizing unmodified oligonucleotides in high quality oligonucleotide array fabrication.     

Molecular genotyping of N-acetylation polymorphism to predict phenotype

Summary N-acetylation polymorphism is one of the representative pharmacogenetic traits that underlie interindividual and interethnic differences in response to xenobiotics. To develop a practical genotyping method to predict acetylator phenotype, we studied the conditions for accurate phenotyping, and identified the phenotype in 51 Japanese. Then we performed Southern blot analysis of genomic DNA from these subjects using ³²P-labeled cDNA for polymorphic N-acetyltransferase in the liver, and found that four N-acetyltransferase alleles generated six genotypes. The present genotyping method predicted the rapid, intermediate, and slow acetylators correctly in 48 of 51 overall subjects (96%) and in all of 4 slow acetylators.     

Transgenic animal technology: alternatives in genotyping and phenotyping

Over the past decade, breakthrough technologies in transgenic animal technology and functional genomics have played a central role in the explosive growth of rodent modeling and in scientific innovation. Various noninvasive alternatives to routine surgical biopsy have been described for genotypic and phenotypic analyses of laboratory animals. A number of options are available to refine or replace potentially painful and invasive procedures ranging from tissue biopsies (including tail biopsies and toe docking) to several blood sampling techniques. Unfortunately, adoption of many non- or minimally invasive alternatives has proven difficult on a number of fronts ranging from historical reservations to procedural expectations and actual experimental productivity. Similarly, a variety of phenotyping considerations have addressed throughput efficiencies and the health and well being of research animals. From an animal welfare perspective, marked increases in laboratory animal populations have accompanied rapid advancements spanning the life sciences. As described for rodent modeling, but with applications across many laboratory animal species, diverse procedural refinements are available that will readily aid in the analysis of whole animal models. Ultimately, non-invasive technologies and complementary refinements have bearing on the quality and reproducibility of data that are reported, as well as of critical importance to the well being and ethical management of animals at all developmental stages: from fetal existence, to the neonatal period, and on through adulthood.     

PanSNPdb: the Pan-Asian SNP genotyping database

The HUGO Pan-Asian SNP consortium conducted the largest survey to date of human genetic diversity among Asians by sampling 1,719 unrelated individuals among 71 populations from China, India, Indonesia, Japan, Malaysia, the Philippines, Singapore, South Korea, Taiwan, and Thailand. We have constructed a database (PanSNPdb), which contains these data and various new analyses of them. PanSNPdb is a research resource in the analysis of the population structure of Asian peoples, including linkage disequilibrium patterns, haplotype distributions, and copy number variations. Furthermore, PanSNPdb provides an interactive comparison with other SNP and CNV databases, including HapMap3, JSNP, dbSNP and DGV and thus provides a comprehensive resource of human genetic diversity. The information is accessible via a widely accepted graphical interface used in many genetic variation databases. Unrestricted access to PanSNPdb and any associated files is available at: http://www4a.biotec.or.th/PASNP.     

Analysis of complex genetic systems by ARMS-SSCP: application to HLA genotyping.

We have developed a new method for analysis of complex genetic systems by a combination of the Amplification Refractory Mutation System (ARMS) and Single-Strand Conformation Polymorphism (SSCP) analysis: ARMS-SSCP. Thus, a complex allelic series is subdivided into a number of groups by ARMS followed by the identification of specific alleles using SSCP analysis. We have shown that the HLA alleles at the DRB3 and DQB1 loci were distinguishable from each other using ARMS-SSCP. In 36 individuals typed for the DRB3 and 48 individuals typed for the DQB1 loci, ARMS-SSCP results were in complete agreement with those obtained using the established method of sequence-specific oligonucleotide (SSO) hybridisation. With silver staining, ARMS-SSCP is a rapid, non-radioactive and reliable method which also offers the possibility for detecting new HLA alleles. We have demonstrated that ARMS-SSCP can be performed using fluorescent PCR primers, a feature which gives the method potential for automation.     

Targeted genotyping by sequencing: a new way to genome profile the cat

Targeted GBS is a recent approach for obtaining an effective characterization for hundreds to thousands of markers. The high throughput of next‐generation sequencing technologies, moreover, allows sample multiplexing. The aims of this study were to (i) define a panel of single nucleotide polymorphisms (SNPs) in the cat, (ii) use GBS for profiling 16 cats, and (iii) evaluate the performance with respect to the inference using standard approaches at different coverage thresholds, thereby providing useful information for designing similar experiments. Probes for sequencing 230 variants were designed based on the Felis_catus_8.0. 8.0 genome. The regions comprised anonymous and non‐anonymous SNPs. Sixteen cat samples were analysed, some of which had already been genotyped in a large group of loci and one having been whole‐genome sequenced in the 99_Lives Cat Genome Sequencing Project. The accuracy of the method was assessed by comparing the GBS results with the genotypes already available. Overall, GBS achieved good performance, with 92–96% correct assignments, depending on the coverage threshold used to define the set of trustable genotypes. Analyses confirmed that (i) the reliability of the inference of each genotype depends on the coverage at that locus and (ii) the fraction of target loci whose genotype can be inferred correctly is a function of the total coverage. GBS proves to be a valid alternative to other methods. Data suggested a depth of less than 11× is required for greater than 95% accuracy. However, sequencing depth must be adapted to the total size of the targets to ensure proper genotype inference.     

SNP genotyping on pooled DNAs: comparison of genotyping technologies and a semi automated method for data storage and analysis

We have compared the accuracy, efficiency and robustness of three methods of genotyping single nucleotide polymorphisms on pooled DNAs. We conclude that (i) the frequencies of the two alleles in pools should be corrected with a factor for unequal allelic amplification, which should be estimated from the mean ratio of a set of heterozygotes (k); (ii) the repeatability of an assay is more important than pinpoint accuracy when estimating allele frequencies, and assays should therefore be optimised to increase the repeatability; and (iii) the size of a pool has a relatively small effect on the accuracy of allele frequency estimation. We therefore recommend that large pools are genotyped and replicated a minimum of four times. In addition, we describe statistical approaches to allow rigorous comparison of DNA pool results. Finally, we describe an extension to our ACeDB database that facilitates management and analysis of the data generated by association studies.     

A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome

SNP genotyping arrays have been useful for many applications that require a large number of molecular markers such as high-density genetic mapping, genome-wide association studies (GWAS), and genomic selection. We report the establishment of a large maize SNP array and its use for diversity analysis and high density linkage mapping. The markers, taken from more than 800,000 SNPs, were selected to be preferentially located in genes and evenly distributed across the genome. The array was tested with a set of maize germplasm including North American and European inbred lines, parent/F1 combinations, and distantly related teosinte material. A total of 49,585 markers, including 33,417 within 17,520 different genes and 16,168 outside genes, were of good quality for genotyping, with an average failure rate of 4% and rates up to 8% in specific germplasm. To demonstrate this array's use in genetic mapping and for the independent validation of the B73 sequence assembly, two intermated maize recombinant inbred line populations - IBM (B73×Mo17) and LHRF (F2×F252) - were genotyped to establish two high density linkage maps with 20,913 and 14,524 markers respectively. 172 mapped markers were absent in the current B73 assembly and their placement can be used for future improvements of the B73 reference sequence. Colinearity of the genetic and physical maps was mostly conserved with some exceptions that suggest errors in the B73 assembly. Five major regions containing non-colinearities were identified on chromosomes 2, 3, 6, 7 and 9, and are supported by both independent genetic maps. Four additional non-colinear regions were found on the LHRF map only; they may be due to a lower density of IBM markers in those regions or to true structural rearrangements between lines. Given the array's high quality, it will be a valuable resource for maize genetics and many aspects of maize breeding.     

An innovative SNP genotyping method adapting to multiple platforms and throughputs

Key message

An innovative genotyping method designated as semi-thermal asymmetric reverse PCR (STARP) was developed for genotyping individual SNPs with improved accuracy, flexible throughputs, low operational costs, and high platform compatibility.

Abstract

Multiplex chip-based technology for genome-scale genotyping of single nucleotide polymorphisms (SNPs) has made great progress in the past two decades. However, PCR-based genotyping of individual SNPs still remains problematic in accuracy, throughput, simplicity, and/or operational costs as well as the compatibility with multiple platforms. Here, we report a novel SNP genotyping method designated semi-thermal asymmetric reverse PCR (STARP). In this method, genotyping assay was performed under unique PCR conditions using two universal priming element-adjustable primers (PEA-primers) and one group of three locus-specific primers: two asymmetrically modified allele-specific primers (AMAS-primers) and their common reverse primer. The two AMAS-primers each were substituted one base in different positions at their 3′ regions to significantly increase the amplification specificity of the two alleles and tailed at 5′ ends to provide priming sites for PEA-primers. The two PEA-primers were developed for common use in all genotyping assays to stringently target the PCR fragments generated by the two AMAS-primers with similar PCR efficiencies and for flexible detection using either gel-free fluorescence signals or gel-based size separation. The state-of-the-art primer design and unique PCR conditions endowed STARP with all the major advantages of high accuracy, flexible throughputs, simple assay design, low operational costs, and platform compatibility. In addition to SNPs, STARP can also be employed in genotyping of indels (insertion–deletion polymorphisms). As vast variations in DNA sequences are being unearthed by many genome sequencing projects and genotyping by sequencing, STARP will have wide applications across all biological organisms in agriculture, medicine, and forensics.

     

How to track and assess genotyping errors in population genetics studies

Genotyping errors occur when the genotype determined after molecular analysis does not correspond to the real genotype of the individual under consideration. Virtually every genetic data set includes some erroneous genotypes, but genotyping errors remain a taboo subject in population genetics, even though they might greatly bias the final conclusions, especially for studies based on individual identification. Here, we consider four case studies representing a large variety of population genetics investigations differing in their sampling strategies (noninvasive or traditional), in the type of organism studied (plant or animal) and the molecular markers used [microsatellites or amplified fragment length polymorphisms (AFLPs)]. In these data sets, the estimated genotyping error rate ranges from 0.8% for microsatellite loci from bear tissues to 2.6% for AFLP loci from dwarf birch leaves. Main sources of errors were allelic dropouts for microsatellites and differences in peak intensities for AFLPs, but in both cases human factors were non-negligible error generators. Therefore, tracking genotyping errors and identifying their causes are necessary to clean up the data sets and validate the final results according to the precision required. In addition, we propose the outline of a protocol designed to limit and quantify genotyping errors at each step of the genotyping process. In particular, we recommend (i) several efficient precautions to prevent contaminations and technical artefacts; (ii) systematic use of blind samples and automation; (iii) experience and rigor for laboratory work and scoring; and (iv) systematic reporting of the error rate in population genetics studies.     

Antibody-based approach to high-volume genotyping for MIC-1 polymorphism

Macrophage inhibitory cytokine-1 (MIC-1) is a divergent member of the TGF-beta superfamily. There are at least two known alleles of MIC-1 that are due to a G-->C point substitution at position 6 of the mature protein, which alters a histidine to an aspartic acid (MIC-1 H and MIC-1 D). We have determined the phenotype of MIC-1 circulating in serum by exploiting the differences in the affinity of the two monoclonal antibodies to the H and D alleles of MIC-1. A PCR-RFLP-based method for genotyping MIC-1 is also described. We validate these two assays using DNA sequencing of 19 subjects as the standard. We then used the validated assay to determine the frequency of the two MIC-1 alleles in a population of 261 adult blood donors. Inter-assay and sequencing concordance was 100%. The frequency of the three common MIC-1 genotypes was homozygous (HH), 54%; heterozygous (HD), 39%; and homozygous (DD), 7%. This novel antibody-based assay confidently determines the genotype of MIC-1. It offers the advantages of an ELISA-ease of automation, high-volume throughput of samples, and ease of use in a routine, clinical laboratory.